20.500.12556/DKUM-94527 Ensemble-based knowledge distillation for identification of childhood pneumonia Childhood pneumonia remains a key cause of global morbidity and mortality, highlighting the need for accurate and efficient diagnostic tools. Ensemble methods have proven to be among the most successful approaches for identifying childhood pneumonia from chest X-ray images. However, deploying large, complex convolutional neural network models in resource-constrained environments presents challenges due to their high computational demands. Therefore, we propose a novel ensemble-based knowledge distillation method for identifying childhood pneumonia from X-ray images, which utilizes an ensemble of classification models to distill the knowledge to a more efficient student model. Experiments conducted on a chest X-ray dataset show that the distilled student model achieves comparable (statistically not significantly different) predictive performance to that of the Stochastic Gradient with Warm Restarts ensemble method (F1-score on average 0.95 vs. 0.96, respectively), while significantly reducing inference time and decreasing FLOPs by a factor of 6.5. Based on the obtained results, the proposed method highlights the potential of knowledge distillation to enhance the efficiency of complex methods, making them more suitable for utilization in environments with limited computational resources. knowledge distillation convolutional neural networks childhood pneumonia konvolucijske nevronske mreže destilacija znanja otroška pljučnica true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/electronics © 2025 by the authors Članek v reviji 2025-08-20 12:21:08 2025-08-20 12:21:09 2025-08-21 03:09:44 0000-00-00 00:00:00 2025 0 0 21 str. iss. 15, [art. no.] 3115 Vol. 14 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2025-05-26 2025-08-03 2025-08-05 004.9 246010371 10.3390/electronics14153115 2079-9292 electronics-14-03115.pdf electronics-14-03115.pdf 1 9C1E2F1449121390503F2B2AA8CE2C30 bb1d0a9532a474eba4a9ccaad0a7c44a4cc26782d80b36122185e8b236a3fa53 a6b95ab0-7daf-11f0-a14c-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=195700 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0